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Databricks Certified Data Engineer Professional Sample Questions:

1. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
E) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

2. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta OPTIMIZE
B) Delta Time Travel
C) Delta APPEND_ONLY
D) Delta VACUUM

3. A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A) Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
B) Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C) Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
D) Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
E) Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

4. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
B) Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
C) Schedule a job to execute the pipeline once an hour on a new job cluster.
D) Configure a job that executes every time new data lands in a given directory.

5. A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

A) Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
B) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
C) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
D) Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

Solutions: